Periodontitis, a chronic inflammatory disease of the periodontium, affects an estimated 50% of US adults over the age of 35. To develop appropriate planning for treatment and cure, dental health care professionals must understand the biological, socio- demographic, behavioral and other medical factors that affect tooth loss through periodontal progression. However, the statistical methods employed to understand these come with several interesting challenges. The data are multivariate, non-Gaussian, non- stationary and have missing information which are informative of the oral health status of that oral region. Besides, one can conjecture that periodontal progression can be spatially referenced. Current statistical methods do not address all of these under a unified framework. Goals: Using a Bayesian paradigm, the proposed study will develop robust statistical methods combining all the above challenges, for assessing periodontal disease status and identifying important covariates that are associated. Subjects: The statistical methods will be evaluated on a dataset of 313 dentate subjects who were enrolled in the Gullah African-American (AA) Diabetics (GAAD) Study as part of the SC COBRE for Oral Health. For generalizability, the methods will be investigated on nationally-representative data collected as part of NHANES (1999-2004). Available data and study design: Periodontal status (determined by site-level pocket dept, clinical attachment level, and bleeding on probing), other relevant biological and medical status like smoking habits, brushing and flossing habits, demographics (poverty status) and other parameters have been collected at the Medical University of South Carolina (MUSC) as part of the GAAD study. The Gullah-AA subjects represent an interesting population with minimal genetic admixture whose dental health status remains vastly unknown. NHANES data are publicly available. Significance: The new statistical methods will provide dental researchers enhanced knowledge about how spatial associations might predict periodontal progression in presence of the aforementioned characteristics typical for periodontal data. This will enable researchers to better target risk assessment and prevention strategies, thereby improving health status.